Cannabinoids for Cancer-related Pain Management: An Update on Therapeutic Applications and Future Perspectives.

Pain is a debilitating phenomenon that dramatically impairs the quality of life of patients. Many chronic conditions, including cancer, are associated with chronic pain. Despite pharmacological efforts that have been conducted, many patients suffering from cancer pain remain without treatment. To date, opioids are considered the preferred therapeutic choice for cancer-related pain management. Unfortunately, opioid treatment causes side effects and inefficiently relieves patients from pain, therefore alternative therapies have been considered, including Cannabis Sativa and cannabinoids. Accumulating evidence has highlighted that an increasing number of patients are choosing to use cannabis and cannabinoids for the management of their soothing and non-palliative cancer pain and other cancer-related symptoms. However, their clinical application must be supported by convincing and reproducible clinical trials. In this review, we provide an update on cannabinoid use for cancer pain management. Moreover, we tried to turn a light on the potential use of cannabis as a possible therapeutic option for cancer-related pain relief.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives.

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

[The body of pain. The experience of pain in the cancer patient.] / Il "corpo di dolore". L'esperienza del dolore nel paziente oncologico.

In oncological disease, a definition of pain as total suffering emerges. This complex phenomenon is characterized by the simultaneous involvement of several dimensions (bodily, cognitive, emotional, family, social, and cultural) held together by a bond of mutual interdependence. Cancer pain is pervasive and affects all aspects of a person's life. It alters the individual's perspective and perception of the world, creating a sense of stagnation and uncertainty characterized by anguish and precariousness. It threatens the sense of personal identity and extends its influence on the entire relational system in which the patient is inserted. The pain of the individual affects the entire family system: priorities, needs, rhythms, methods of communication, and family relationships change to cope with the devastating pathological condition. The relationship between pain and emotions is close as cancer pain stimulates powerful emotional experiences that exert a significant influence on the modalities the patient adopts to manage pain. In addition to the emotional aspects, cognitive factors contribute to characterizing the pain experience: each individual has a set of beliefs, convictions, expectations, and ways of understanding pain built on his/her life experiences and socio-cultural context. Careful understanding of these aspects is of fundamental importance in clinical practice as they modulate the whole pain experience. Furthermore, the patient's experiences with pain can affect the overall response to the disease and have a negative impact on functionality and well-being. Consequently, the impact of cancer pain extends to the patient's family and social network. Because of the multicomponent nature of the cancer pain phenomenon, there is a need for an integrated and multidimensional approach to its study and treatment. This approach must provide for the activation of a flexible setting oriented to the patient's global care and encompassing the totality of the biopsychosocial needs. In addition to the symptom assessment, recognizing the "person" is the challenge that must be taken in the authentic space of a relationship that nourishes and sustains itself. The aim is to go through the experience of pain together with the patient in the direction of consolation and hope.

Implementation of a Hybrid Care Model for Telemedicine-based Cancer Pain Management at the Cancer Center of Naples, Italy: A Cohort Study.

BACKGROUND/AIM: Telemedicine, the remote delivery of healthcare services, represents a great opportunity for cancer pain management. A care model of telemedicine that combines remote visits and hospital access could be an effective and safe strategy for pain management of cancer patients. PATIENTS AND METHODS: A retrospective study was conducted using the dataset of the telemedicine program at the Istituto Nazionale Tumori of Naples, Italy for assessing the efficacy of a telehealth-based model of care. Demographic, clinical, and process variables were investigated. RESULTS: A total of 226 cases and 489 visits were included in the analysis. The mean age of patients was 63.4 years (SD=12.4 years), and no sex differences were observed. Approximately 55% of patients were ECOG-PS ≤2 and 87% suffered from metastatic disease. More than half of the patients were treated with high doses of opioids. Each patient had a mean of 2 remote visits and half of the patients had more than 1 telehealth consultation. The dropout ratio was 5.3%. Most visits (n=472) were conducted on patients in the Campania Region, Italy. The maximum covered distance from the Cancer Center and the patients' location was 555.22 Km. A significant difference in the overall number of visits (p=0.006) and the number of pro-capita remote visits (p=0.010) was found, in favor of the group of patients treated before the end of the Covid-19 emergency in Italy, compared to those treated after the pandemic. CONCLUSION: Despite various positive outcomes, the analysis highlights several weaknesses, such as the need to assist patients with advanced and non-advanced disease located outside the regional territory. Overall, the telehealth processes should be adapted to post-pandemic scenarios towards their implementation in routine clinical practice.

Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies.

Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained from a single-center program of telemedicine-based cancer pain management. These models included random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN), and the LASSO−RIDGE algorithm. Thirteen demographic, social, clinical, and therapeutic variables were adopted to define the conditions that can affect the number of teleconsultations. After ML validation, the risk analysis for more than one remote consultation was assessed in target individuals. Results: The data from 158 patients were collected. In the training set, the accuracy was about 95% and 98% for ANN and RF, respectively. Nevertheless, the best accuracy on the test set was obtained with RF (70%). The ML-based simulations showed that young age (<55 years), lung cancer, and occurrence of breakthrough cancer pain help to predict the number of remote consultations. Elderly patients (>75 years) with bone metastases may require more telemedicine-based clinical evaluations. Conclusion: ML-based analyses may enable clinicians to identify the best model for predicting the need for more remote consultations. It could be useful for calibrating care interventions and resource allocation.

Inductively coupled plasma mass spectrometry and standard dilution analysis applied to concentrated acids.

This work describes a procedure using the recently proposed standard dilution analysis (SDA) calibration method for the determination of As, Cr and Ni in concentrated HNO3 and HCl by inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). Because of the low contaminant levels, and consequently low limits of detection required for these reagents (commonly used in trace element analysis and the semiconductor industry), samples were minimally diluted. The analysis of concentrated acids can result in matrix/transport effects, which may compromise accuracy in ICP-MS determinations. High-chlorine content samples are also challenging for As and Cr determination due to the formation of polyatomic species such as 40Ar35Cl+ and 35Cl16OH+, which overlap the only As isotope, 75As+, and the main Cr isotope, 52Cr+, respectively. The combination of SDA and ICP-MS/MS was evaluated to overcome matrix, transport and spectral interferences in order to increase accuracy, precision and sample throughput. The performance of SDA was compared with the traditional methods of external standard calibration (EC), internal standardization (IS), and standard additions (SA). The limits of detection for SDA were calculated as 6, 10, and 30ngL-1 for As, Cr, and Ni, respectively. Recoveries for spike experiments using the new method were in the 90-114% range for all analytes. The procedure described here provides similar or even better analytical performance in comparison with EC, IS and SA.

Determination of sulfur in biodiesel microemulsions using the summation of the intensities of multiple emission lines.

A method for the determination of sulfur in biodiesel samples by inductively coupled plasma optical emission spectrometry which uses microemulsion for sample preparation and the summation of the intensities of multiple emission lines has been developed. Microemulsions were prepared using 0.5 mL of 20% v/v HNO(3), 0.5 mL of Triton X-100, 2-3 mL of biodiesel sample, and diluted with n-propanol to a final volume of 10 mL. Summation of the emission intensities of multiple sulfur lines allowed for increased accuracy and sensitivity. The amounts of sulfur determined experimentally were between 2 and 7 mg L(-1), well below legislative standards for many countries. Recoveries obtained ranged from 72 to 119%, and recoveries obtained for the 182.562 nm line were slightly lower. This is most likely due to its lower sensitivity. Using microemulsion for sample preparation and the summation of the intensities of multiple emission lines for the successful determination of sulfur in biodiesel has been demonstrated.

Direct determination of Cd, Cu and Pb in wines and grape juices by thermospray flame furnace atomic absorption spectrometry.

The applicability of thermospray flame furnace atomic absorption spectrometry (TS-FF-AAS) was evaluated for direct determination of Cu, Cd and Pb in wines and grape juices. The developed procedure does not require preliminary acid digestion of the samples. The optimum conditions for determination of Cu, Cd and Pb in wines were studied and the performance was compared to those typically obtained by flame atomic absorption spectrometry (FAAS). A sample volume of 150 microL was introduced into a heated nickel tube at a flow rate of 0.54 mLmin(-1) and 0.14 molL(-1) HNO(3) was used as sample carrier flowing at 2.5 mLmin(-1) for determining all analytes. The effect of ethanol concentrations on Cu, Cd and Pb absorbance signals were studied. All determinations were carried out by adopting optimized conditions and quantification was based on the standard additions method. Limits of detection (LOD) of 12.9, 1.8 and 5.3 microgL(-1) (n=14) for Cu, Cd and Pb, respectively, were obtained for wine samples (3sigma(blank)/slope, n=14). Relative standard deviations (R.S.D., %) of 2.7, 2.1 and 2.6 for Cu, Cd and Pb, were obtained (n=6) for wine samples. The values determined for grape juice samples were similar to these ones. The analytical throughput was 45 determinations h(-1) and accuracy was checked by addition-recovery experiments.